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Fix accuracy metric (elastic#50310)
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@przemekwitek @SivagurunathanV
przemekwitek authored and SivagurunathanV committed Jan 21, 2020
1 parent 5ed3bd1 commit c0ec3ef
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Showing 14 changed files with 472 additions and 295 deletions.
97 changes: 53 additions & 44 deletions .../java/org/elasticsearch/client/ml/dataframe/evaluation/classification/AccuracyMetric.java
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Expand Up @@ -20,6 +20,7 @@

import org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric;
import org.elasticsearch.common.ParseField;
import org.elasticsearch.common.Strings;
import org.elasticsearch.common.xcontent.ConstructingObjectParser;
import org.elasticsearch.common.xcontent.ObjectParser;
import org.elasticsearch.common.xcontent.ToXContent;
Expand All @@ -35,10 +36,25 @@
import static org.elasticsearch.common.xcontent.ConstructingObjectParser.constructorArg;

/**
* {@link AccuracyMetric} is a metric that answers the question:
* "What fraction of examples have been classified correctly by the classifier?"
* {@link AccuracyMetric} is a metric that answers the following two questions:
*
* equation: accuracy = 1/n * Σ(y == y´)
* 1. What is the fraction of documents for which predicted class equals the actual class?
*
* equation: overall_accuracy = 1/n * Σ(y == y')
* where: n = total number of documents
* y = document's actual class
* y' = document's predicted class
*
* 2. For any given class X, what is the fraction of documents for which either
* a) both actual and predicted class are equal to X (true positives)
* or
* b) both actual and predicted class are not equal to X (true negatives)
*
* equation: accuracy(X) = 1/n * (TP(X) + TN(X))
* where: X = class being examined
* n = total number of documents
* TP(X) = number of true positives wrt X
* TN(X) = number of true negatives wrt X
*/
public class AccuracyMetric implements EvaluationMetric {

Expand Down Expand Up @@ -78,29 +94,29 @@ public int hashCode() {

public static class Result implements EvaluationMetric.Result {

private static final ParseField ACTUAL_CLASSES = new ParseField("actual_classes");
private static final ParseField CLASSES = new ParseField("classes");
private static final ParseField OVERALL_ACCURACY = new ParseField("overall_accuracy");

@SuppressWarnings("unchecked")
private static final ConstructingObjectParser<Result, Void> PARSER =
new ConstructingObjectParser<>("accuracy_result", true, a -> new Result((List<ActualClass>) a[0], (double) a[1]));
new ConstructingObjectParser<>("accuracy_result", true, a -> new Result((List<PerClassResult>) a[0], (double) a[1]));

static {
PARSER.declareObjectArray(constructorArg(), ActualClass.PARSER, ACTUAL_CLASSES);
PARSER.declareObjectArray(constructorArg(), PerClassResult.PARSER, CLASSES);
PARSER.declareDouble(constructorArg(), OVERALL_ACCURACY);
}

public static Result fromXContent(XContentParser parser) {
return PARSER.apply(parser, null);
}

/** List of actual classes. */
private final List<ActualClass> actualClasses;
/** Fraction of documents predicted correctly. */
/** List of per-class results. */
private final List<PerClassResult> classes;
/** Fraction of documents for which predicted class equals the actual class. */
private final double overallAccuracy;

public Result(List<ActualClass> actualClasses, double overallAccuracy) {
this.actualClasses = Collections.unmodifiableList(Objects.requireNonNull(actualClasses));
public Result(List<PerClassResult> classes, double overallAccuracy) {
this.classes = Collections.unmodifiableList(Objects.requireNonNull(classes));
this.overallAccuracy = overallAccuracy;
}

Expand All @@ -109,8 +125,8 @@ public String getMetricName() {
return NAME;
}

public List<ActualClass> getActualClasses() {
return actualClasses;
public List<PerClassResult> getClasses() {
return classes;
}

public double getOverallAccuracy() {
Expand All @@ -120,7 +136,7 @@ public double getOverallAccuracy() {
@Override
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
builder.startObject();
builder.field(ACTUAL_CLASSES.getPreferredName(), actualClasses);
builder.field(CLASSES.getPreferredName(), classes);
builder.field(OVERALL_ACCURACY.getPreferredName(), overallAccuracy);
builder.endObject();
return builder;
Expand All @@ -131,52 +147,42 @@ public boolean equals(Object o) {
if (this == o) return true;
if (o == null || getClass() != o.getClass()) return false;
Result that = (Result) o;
return Objects.equals(this.actualClasses, that.actualClasses)
return Objects.equals(this.classes, that.classes)
&& this.overallAccuracy == that.overallAccuracy;
}

@Override
public int hashCode() {
return Objects.hash(actualClasses, overallAccuracy);
return Objects.hash(classes, overallAccuracy);
}
}

public static class ActualClass implements ToXContentObject {
public static class PerClassResult implements ToXContentObject {

private static final ParseField ACTUAL_CLASS = new ParseField("actual_class");
private static final ParseField ACTUAL_CLASS_DOC_COUNT = new ParseField("actual_class_doc_count");
private static final ParseField CLASS_NAME = new ParseField("class_name");
private static final ParseField ACCURACY = new ParseField("accuracy");

@SuppressWarnings("unchecked")
private static final ConstructingObjectParser<ActualClass, Void> PARSER =
new ConstructingObjectParser<>("accuracy_actual_class", true, a -> new ActualClass((String) a[0], (long) a[1], (double) a[2]));
private static final ConstructingObjectParser<PerClassResult, Void> PARSER =
new ConstructingObjectParser<>("accuracy_per_class_result", true, a -> new PerClassResult((String) a[0], (double) a[1]));

static {
PARSER.declareString(constructorArg(), ACTUAL_CLASS);
PARSER.declareLong(constructorArg(), ACTUAL_CLASS_DOC_COUNT);
PARSER.declareString(constructorArg(), CLASS_NAME);
PARSER.declareDouble(constructorArg(), ACCURACY);
}

/** Name of the actual class. */
private final String actualClass;
/** Number of documents (examples) belonging to the {code actualClass} class. */
private final long actualClassDocCount;
/** Fraction of documents belonging to the {code actualClass} class predicted correctly. */
/** Name of the class. */
private final String className;
/** Fraction of documents that are either true positives or true negatives wrt {@code className}. */
private final double accuracy;

public ActualClass(
String actualClass, long actualClassDocCount, double accuracy) {
this.actualClass = Objects.requireNonNull(actualClass);
this.actualClassDocCount = actualClassDocCount;
public PerClassResult(String className, double accuracy) {
this.className = Objects.requireNonNull(className);
this.accuracy = accuracy;
}

public String getActualClass() {
return actualClass;
}

public long getActualClassDocCount() {
return actualClassDocCount;
public String getClassName() {
return className;
}

public double getAccuracy() {
Expand All @@ -186,8 +192,7 @@ public double getAccuracy() {
@Override
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
builder.startObject();
builder.field(ACTUAL_CLASS.getPreferredName(), actualClass);
builder.field(ACTUAL_CLASS_DOC_COUNT.getPreferredName(), actualClassDocCount);
builder.field(CLASS_NAME.getPreferredName(), className);
builder.field(ACCURACY.getPreferredName(), accuracy);
builder.endObject();
return builder;
Expand All @@ -197,15 +202,19 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
public boolean equals(Object o) {
if (this == o) return true;
if (o == null || getClass() != o.getClass()) return false;
ActualClass that = (ActualClass) o;
return Objects.equals(this.actualClass, that.actualClass)
&& this.actualClassDocCount == that.actualClassDocCount
PerClassResult that = (PerClassResult) o;
return Objects.equals(this.className, that.className)
&& this.accuracy == that.accuracy;
}

@Override
public int hashCode() {
return Objects.hash(actualClass, actualClassDocCount, accuracy);
return Objects.hash(className, accuracy);
}

@Override
public String toString() {
return Strings.toString(this);
}
}
}
14 changes: 7 additions & 7 deletions client/rest-high-level/src/test/java/org/elasticsearch/client/MachineLearningIT.java
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Expand Up @@ -1819,15 +1819,15 @@ public void testEvaluateDataFrame_Classification() throws IOException {
AccuracyMetric.Result accuracyResult = evaluateDataFrameResponse.getMetricByName(AccuracyMetric.NAME);
assertThat(accuracyResult.getMetricName(), equalTo(AccuracyMetric.NAME));
assertThat(
accuracyResult.getActualClasses(),
accuracyResult.getClasses(),
equalTo(
List.of(
// 3 out of 5 examples labeled as "cat" were classified correctly
new AccuracyMetric.ActualClass("cat", 5, 0.6),
// 3 out of 4 examples labeled as "dog" were classified correctly
new AccuracyMetric.ActualClass("dog", 4, 0.75),
// no examples labeled as "ant" were classified correctly
new AccuracyMetric.ActualClass("ant", 1, 0.0))));
// 9 out of 10 examples were classified correctly
new AccuracyMetric.PerClassResult("ant", 0.9),
// 6 out of 10 examples were classified correctly
new AccuracyMetric.PerClassResult("cat", 0.6),
// 8 out of 10 examples were classified correctly
new AccuracyMetric.PerClassResult("dog", 0.8))));
assertThat(accuracyResult.getOverallAccuracy(), equalTo(0.6)); // 6 out of 10 examples were classified correctly
}
{ // Precision
Expand Down
8 changes: 4 additions & 4 deletions ...lasticsearch/client/ml/dataframe/evaluation/classification/AccuracyMetricResultTests.java
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Expand Up @@ -19,7 +19,7 @@
package org.elasticsearch.client.ml.dataframe.evaluation.classification;

import org.elasticsearch.client.ml.dataframe.evaluation.MlEvaluationNamedXContentProvider;
import org.elasticsearch.client.ml.dataframe.evaluation.classification.AccuracyMetric.ActualClass;
import org.elasticsearch.client.ml.dataframe.evaluation.classification.AccuracyMetric.PerClassResult;
import org.elasticsearch.client.ml.dataframe.evaluation.classification.AccuracyMetric.Result;
import org.elasticsearch.common.xcontent.NamedXContentRegistry;
import org.elasticsearch.common.xcontent.XContentParser;
Expand All @@ -41,13 +41,13 @@ protected NamedXContentRegistry xContentRegistry() {
public static Result randomResult() {
int numClasses = randomIntBetween(2, 100);
List<String> classNames = Stream.generate(() -> randomAlphaOfLength(10)).limit(numClasses).collect(Collectors.toList());
List<ActualClass> actualClasses = new ArrayList<>(numClasses);
List<PerClassResult> classes = new ArrayList<>(numClasses);
for (int i = 0; i < numClasses; i++) {
double accuracy = randomDoubleBetween(0.0, 1.0, true);
actualClasses.add(new ActualClass(classNames.get(i), randomNonNegativeLong(), accuracy));
classes.add(new PerClassResult(classNames.get(i), accuracy));
}
double overallAccuracy = randomDoubleBetween(0.0, 1.0, true);
return new Result(actualClasses, overallAccuracy);
return new Result(classes, overallAccuracy);
}

@Override
Expand Down
2 changes: 1 addition & 1 deletion .../src/main/java/org/elasticsearch/xpack/core/ml/dataframe/evaluation/EvaluationMetric.java
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Expand Up @@ -44,5 +44,5 @@ public interface EvaluationMetric extends ToXContentObject, NamedWriteable {
* Gets the evaluation result for this metric.
* @return {@code Optional.empty()} if the result is not available yet, {@code Optional.of(result)} otherwise
*/
Optional<EvaluationMetricResult> getResult();
Optional<? extends EvaluationMetricResult> getResult();
}
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